The scientific induction starter pack.pic.twitter.com/N1Hm1W3tpq
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I know you hate bayesianism but doesn't it solve this particular problem quite cleanly?
Only if you make a bunch of sampling assumptions, which are not realistic in general. This explanation is not great, but it’s a start: https://en.wikipedia.org/wiki/Raven_paradox#Role_of_background_knowledge …
But is that an example of scientific induction though? It doesn’t abstract to an underlying causal system. It’s merely a predictor based on past data.
This is controversial within the philosophy of science. Most theories say that good scientific theories are predictive. “Causality” is pretty much impossible to define. Working scientists are likely to have a different view! But they can’t define causality either.
Why couldn't you gain information about ravens by looking at apples? You would get less information with the green apple than with the black raven, but that is because there are many more non-raven things than there are ravens.
Well… you can start by reading the Wikipedia article? It gets complicated quickly (and the article is only a brief overview of the problems…)
Hempel's resolution seems intuitively correct to me.
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